We live in a world where catastrophes are increasing in occurrence and uncertainty. Global climate change is a reality we cannot easily dismiss. Natural calamities and extreme casualty events can unsettle the financial status of a business, and challenge its resilience.
While such events cannot always be averted, timely and accurate insights can help mitigate the financial impact for insurers. The need to assess and manage the risks with precision and consistency has intensified, pushing the role of property and casualty insurance underwriting into the foreground.
Traditionally, P&C insurance underwriters have relied on historical data. However, in recent years, several events have revealed the inadequacies of this approach in making reliable predictions of future loss ratios. The risks connected to natural disasters have exposed limitations in coverage. In turn, this has made insurance policyholders question their insurer’s capabilities.
As matters stand, the P&C insurance sector is yet to recover from the economic repercussions of recent events. Moreover, insurers now well understood that the quality of insurance underwriting is critical to business performance. These circumstances call out the need for underwriting modernization.
The route to underwriting modernization
Modernizing insurance underwriting is no mean feat and can seem overwhelming. However, insurers have a ready starting point – data.
We live in an increasingly data- and analytics-driven world. In the context of catastrophic events, P&C insurance underwriters will benefit from stronger, end-to-end risk assessment capabilities that encompass vulnerabilities, hazards, financial losses and more.
It’s also not easy to stand out in the highly competitive commercial P&C insurance market, especially in terms of pricing structures. As insurers look to distinguish themselves, data-led improvements in efficiency, decision-making and customer experience in their underwriting process will bolster their potential to excel.
Catastrophe modeling for insurance has been a welcome augmentation to underwriters as it helps them more accurately gauge the probability and extent of losses due to a catastrophic event. The modern-day catastrophe modeling practice offers predictions by using advanced software and a wider range of variables like precision geolocation data or environmental data that was unexplored in traditional methods. As a consequence, insurance underwriters can identify new areas of risk.
A data-driven catastrophe modeling practice for insurance underwriters
The nature of risk associated with catastrophes has changed significantly. It needs to be properly understood by P&C insurance underwriters so they can offer tangible value to customers beyond risk transfer.
Underwriting excellence is a key attribute shared by industry leaders as substantiated by McKinsey research, and must be prioritized by other players.
Building a superior catastrophe modeling practice for insurance underwriters will be a key milestone in this direction. We see great potential in catastrophe modeling tools to go beyond risk evaluation when used optimally by insurance underwriters. In driving these competitive differentiators, the comprehensive use of advanced data and analytics to augment the human element will be key.
Here are some recommended ways to strengthen insurance underwriting and build a data-driven catastrophe modeling practice that leads to enhanced risk evaluation, decision-making, and loss prevention strategies.
Navigating towards excellence in insurance underwriting:
Proactive risk management:
Identifying high-risk zones will help insurers assess the client’s exposure with more accuracy. Geocoding can be used to develop exhaustive risk maps based on location data, georeferenced data and information of the client’s facilities. This will help calculate approximate potential losses so that clients can decide on the most suitable coverage for their P&C insurance needs.
In-depth analysis of risk parameters:
Insurance underwriters need to analyse risks in terms of hazard, vulnerability, exposure and financial aspects to gain a comprehensive understanding of their customers’ operations and facilities as well as risk factors.
Mapping risks with precision:
In the modern context, Catastrophe Modeling for insurance must depend on advanced analytics to enhance their risk prediction and management capabilities.
The way forward with DPA
Decimal Point Analytics provides a comprehensive Catastrophe Modeling service that meticulously quantifies potential financial losses due to disasters, elevating risk management practices, sharpening underwriting decisions, and bolstering solvency in the wake of major catastrophes
We apply advanced analytical tools and the deep domain knowledge of our dedicated team to extract insights with precision and steer our P&C insurance clients through the complexities of catastrophe risk. Working closely with their underwriters, we help them make comprehensive risk assessments and better decisions so that they can offer more relevant risk insurance to their customers.
